The importance of metadata for storage and retrieval of digital images is well recognized. Image metadata is essentially non-picture information that is embedded in the image in addition to the actual image data. The metadata is typically associated with a digital image either by the user or by the image capture device, e.g., a digital camera. The metadata manually provided by the user typically includes keywords, called tags, which may be used to categorize the image for subsequent searching. The metadata provided by image capture device typically includes information about the image, such as the time and date the picture was taken, by whom, with what equipment, and the like. Besides time and date, some image capture devices, such as camera phones, are also capable of automatically recording and storing location information, such as global positioning system (GPS) coordinates.
Although current digital imaging technology enables people to acquire images easily and to record additional information, retrieving the images based on metadata searches is not as easy. The challenge is not how to index the pictures using the metadata, but somehow relating a picture the user is trying to retrieve with some aspects of the user's memory about the picture. Time and location are useful as metadata, but their use to help a user search for images of interest is inherently limited. In many cases, users have a difficult time remembering the specific time or even interval of time when those pictures were taken, particularly for images taken years ago. Thus, using time to find those older pictures is often difficult.
Location may be easier to remember than image capture time, but in many cases, the user may not know the location of where a picture was taken, except within a large geographical region. For example, if a user takes a picture while driving through New Delhi, unless it is close to some famous landmark, the user might not be able to tell where exactly the picture was taken. Later, when the user tries to retrieve that picture, the user may not know what location or range of locations to use as the search terms.
In other cases, the user may have known the location where the picture was taken, but may not remember the details because the memory of the location may have faded in the user's mind. For example, if the user takes a long trip through a nature park, there may be few place names that the user can remember months later. As another example, after vacationing in a city for awhile, all the streets in the city may begin to look about the same to the user.
Searching for images using time and location may seem logical at first, but many users would find it more natural to search for information based on the feelings they had at the time the pictures were taken, or the feelings that the pictures remind them of. Although the user could attempt to solve this problem by entering his or her own metadata as captions that have meaning to them, unless the user remembers to search using exact keywords from those captions, it would still be difficult for the user to retrieve the pictures he or she desires. The metadata should be such that it accords with human memory, so that people can use the metadata to associate pictures with things they remember.
Mor Naaman et al. provide a method for “Automatically Generating Metadata for Digital Photographs with Geographic Coordinates” in which location information for digital photographs is used to automatically generate photo-related metadata that serves as additional memory cues and filters when browsing a collection of photos. For example, given the local time and location for each photo, information such as light status (day, dusk, night and dawn) and weather (rainy, clear, warm) is retrieved and used to generate metadata.
Metadata generated using generic weather data, however, may be too vague for the user to properly recall the information when searching for the following reasons. One reason is that symbolic adjectives for certain categories of weather, such as temperature and humidity (e.g., “hot” and “cold”) is subjective to each person. For example, if someone were taking pictures in Louisiana, the definition of humid would shift toward very humid, while the definition of humid would shift down for someone in Arizona. Thus, using global weather labels for subjective criteria such as temperature and humidity may result in erroneous search results.
In addition, generic weather data tends to be regional and not sufficiently specific to the actual location of the picture. For instance, Naaman et al. generate weather metadata by translating an image's location (latitude, longitude) into a zip code and then uses the zip code and image date to query a weather web service to get weather information. The weather data returned for each day is an hourly report of the weather conditions (e.g., “rainy”, “clear”) and temperature. The temperature is computed as the average of temperatures measured in the hours around the photo time. The result of the query to the weather web service is then used for all photos taken in the same day and same area. The problem is that hourly weather reports of a particular zip code may not be indicative of the actual weather at the location and time of the image capture. For example, a picture of a couple kissing could have been taken just as it stopped raining and just as the sun was coming out (clearing), but the metadata for the picture generated from the weather reports may just say raining, which may not conform to what the couple may remember about the photo.